def __init__(self, matrix=None, **kwargs):
"""Initialize PrecomputedSGKernel
Parameters
----------
matrix : SGKernel or Kernel or ndarray
Kernel matrix to be used
"""
# Convert to appropriate kernel for input
if isinstance(matrix, SGKernel):
k = matrix._k # Take internal shogun
elif isinstance(matrix, Kernel):
k = matrix.as_raw_np() # Convert to NP otherwise
else:
# Otherwise SG would segfault ;-)
k = np.array(matrix)
SGKernel.__init__(self, **kwargs)
if versions['shogun:rev'] >= 4455:
self._k = sgk.CustomKernel(k)
else:
raise RuntimeError, \
"Cannot create PrecomputedSGKernel using current version" \
" of shogun -- please upgrade"
def _run_custom():
"""Run Custom kernel."""
params = {
'name': 'Custom',
'accuracy': 1e-7,
'feature_class': 'simple',
'feature_type': 'Real'
}
dim_square = 7
data = dataop.get_rand(dim_square=dim_square)
feats = featop.get_features(params['feature_class'],
params['feature_type'], data)
data = data['train']
symdata = data + data.T
lowertriangle = numpy.array([
symdata[(x, y)] for x in xrange(symdata.shape[1])
for y in xrange(symdata.shape[0]) if y <= x
])
kern = kernel.CustomKernel()
#kern.init(feats['train'], feats['train']
kern.set_triangle_kernel_matrix_from_triangle(lowertriangle)
km_triangletriangle = kern.get_kernel_matrix()
kern.set_triangle_kernel_matrix_from_full(symdata)
km_fulltriangle = kern.get_kernel_matrix()
kern.set_full_kernel_matrix_from_full(data)
km_fullfull = kern.get_kernel_matrix()
output = {
'kernel_matrix_triangletriangle': km_triangletriangle,
'kernel_matrix_fulltriangle': km_fulltriangle,
'kernel_matrix_fullfull': km_fullfull,
'kernel_symdata': numpy.matrix(symdata),
'kernel_data': numpy.matrix(data),
'kernel_dim_square': dim_square
}
output.update(fileop.get_output(category.KERNEL, params))
fileop.write(category.KERNEL, output)
def _as_raw_sg(kernel):
"""Converts directly to a Shogun kernel"""
return sgk.CustomKernel(kernel.as_raw_np())